Overview

Dataset statistics

Number of variables17
Number of observations166800
Missing cells380
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.6 MiB
Average record size in memory136.0 B

Variable types

Numeric14
Categorical2
Text1

Alerts

City is highly overall correlated with CountyHigh correlation
Clean Alternative Fuel Vehicle Eligibility is highly overall correlated with Electric Range and 1 other fieldsHigh correlation
County is highly overall correlated with City and 2 other fieldsHigh correlation
Electric Range is highly overall correlated with Clean Alternative Fuel Vehicle Eligibility and 2 other fieldsHigh correlation
Electric Utility is highly overall correlated with County and 1 other fieldsHigh correlation
Electric Vehicle Type is highly overall correlated with Clean Alternative Fuel Vehicle Eligibility and 1 other fieldsHigh correlation
Model is highly overall correlated with VIN (1-10)High correlation
Model Year is highly overall correlated with Electric RangeHigh correlation
Postal Code is highly overall correlated with County and 1 other fieldsHigh correlation
VIN (1-10) is highly overall correlated with ModelHigh correlation
State is highly skewed (γ1 = -27.32094877)Skewed
Postal Code is highly skewed (γ1 = -30.09608692)Skewed
2020 Census Tract is highly skewed (γ1 = -26.96677648)Skewed
DOL Vehicle ID has unique valuesUnique
Electric Range has 83517 (50.1%) zerosZeros
Base MSRP has 163437 (98.0%) zerosZeros

Reproduction

Analysis started2024-02-18 18:43:49.224215
Analysis finished2024-02-18 18:45:02.077833
Duration1 minute and 12.85 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

VIN (1-10)
Real number (ℝ)

HIGH CORRELATION 

Distinct260
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean169.79538
Minimum1
Maximum1114
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-02-18T18:45:02.265871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q117
median52
Q3196
95-th percentile1029
Maximum1114
Range1113
Interquartile range (IQR)179

Descriptive statistics

Standard deviation269.25036
Coefficient of variation (CV)1.5857343
Kurtosis5.0770067
Mean169.79538
Median Absolute Deviation (MAD)43
Skewness2.4019061
Sum28321870
Variance72495.754
MonotonicityNot monotonic
2024-02-18T18:45:02.579600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 2904
 
1.7%
4 2748
 
1.6%
3 2730
 
1.6%
5 2690
 
1.6%
2 2620
 
1.6%
11 2508
 
1.5%
10 2500
 
1.5%
7 2499
 
1.5%
8 2496
 
1.5%
12 2496
 
1.5%
Other values (250) 140609
84.3%
ValueCountFrequency (%)
1 1969
1.2%
2 2620
1.6%
3 2730
1.6%
4 2748
1.6%
5 2690
1.6%
6 2904
1.7%
7 2499
1.5%
8 2496
1.5%
9 2439
1.5%
10 2500
1.5%
ValueCountFrequency (%)
1114 1114
0.7%
1090 1090
0.7%
1071 2142
1.3%
1052 1052
0.6%
1041 1041
0.6%
1037 1037
0.6%
1029 1029
0.6%
1022 1022
0.6%
1014 1014
0.6%
993 993
0.6%

County
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49566.95
Minimum1
Maximum86594
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-02-18T18:45:02.882008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile943
Q19847
median86594
Q386594
95-th percentile86594
Maximum86594
Range86593
Interquartile range (IQR)76747

Descriptive statistics

Standard deviation38769.695
Coefficient of variation (CV)0.78216826
Kurtosis-1.9294688
Mean49566.95
Median Absolute Deviation (MAD)0
Skewness-0.12131262
Sum8.2677672 × 109
Variance1.5030892 × 109
MonotonicityNot monotonic
2024-02-18T18:45:03.170266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
86594 86594
51.9%
19570 19570
 
11.7%
12972 12972
 
7.8%
9847 9847
 
5.9%
6042 6042
 
3.6%
5522 5522
 
3.3%
4312 4312
 
2.6%
4039 4039
 
2.4%
2028 2028
 
1.2%
1842 1842
 
1.1%
Other values (39) 14032
 
8.4%
ValueCountFrequency (%)
1 86
0.1%
2 54
< 0.1%
3 27
 
< 0.1%
4 40
< 0.1%
5 25
 
< 0.1%
6 24
 
< 0.1%
7 14
 
< 0.1%
8 16
 
< 0.1%
9 18
 
< 0.1%
10 10
 
< 0.1%
ValueCountFrequency (%)
86594 86594
51.9%
19570 19570
 
11.7%
12972 12972
 
7.8%
9847 9847
 
5.9%
6042 6042
 
3.6%
5522 5522
 
3.3%
4312 4312
 
2.6%
4039 4039
 
2.4%
2028 2028
 
1.2%
1842 1842
 
1.1%

City
Real number (ℝ)

HIGH CORRELATION 

Distinct208
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6920.5447
Minimum1
Maximum27831
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-02-18T18:45:03.484652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile140
Q1859
median2597
Q36032
95-th percentile27831
Maximum27831
Range27830
Interquartile range (IQR)5173

Descriptive statistics

Standard deviation9606.3058
Coefficient of variation (CV)1.3880852
Kurtosis0.8247084
Mean6920.5447
Median Absolute Deviation (MAD)2239
Skewness1.5953872
Sum1.1543468 × 109
Variance92281111
MonotonicityNot monotonic
2024-02-18T18:45:03.847383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27831 27831
 
16.7%
8364 8364
 
5.0%
6032 6032
 
3.6%
5869 5869
 
3.5%
5440 5440
 
3.3%
5028 5028
 
3.0%
4876 4876
 
2.9%
4617 4617
 
2.8%
4058 4058
 
2.4%
3510 3510
 
2.1%
Other values (198) 91175
54.7%
ValueCountFrequency (%)
1 227
0.1%
2 150
0.1%
3 90
 
0.1%
4 88
 
0.1%
5 85
 
0.1%
6 60
 
< 0.1%
7 105
0.1%
8 80
 
< 0.1%
9 54
 
< 0.1%
10 60
 
< 0.1%
ValueCountFrequency (%)
27831 27831
16.7%
8364 8364
 
5.0%
6032 6032
 
3.6%
5869 5869
 
3.5%
5440 5440
 
3.3%
5028 5028
 
3.0%
4876 4876
 
2.9%
4617 4617
 
2.8%
4058 4058
 
2.4%
3510 3510
 
2.1%

State
Real number (ℝ)

SKEWED 

Distinct44
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.953543
Minimum0
Maximum43
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-02-18T18:45:04.279738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile42
Q142
median42
Q342
95-th percentile42
Maximum43
Range43
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1704863
Coefficient of variation (CV)0.027899582
Kurtosis774.16708
Mean41.953543
Median Absolute Deviation (MAD)0
Skewness-27.320949
Sum6997851
Variance1.3700382
MonotonicityNot monotonic
2024-02-18T18:45:04.675252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
42 166440
99.8%
7 91
 
0.1%
41 38
 
< 0.1%
23 32
 
< 0.1%
39 24
 
< 0.1%
27 14
 
< 0.1%
17 13
 
< 0.1%
8 12
 
< 0.1%
12 10
 
< 0.1%
14 9
 
< 0.1%
Other values (34) 117
 
0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1
 
< 0.1%
2 5
 
< 0.1%
3 1
 
< 0.1%
4 2
 
< 0.1%
5 7
 
< 0.1%
6 3
 
< 0.1%
7 91
0.1%
8 12
 
< 0.1%
9 7
 
< 0.1%
ValueCountFrequency (%)
43 1
 
< 0.1%
42 166440
99.8%
41 38
 
< 0.1%
40 3
 
< 0.1%
39 24
 
< 0.1%
38 7
 
< 0.1%
37 1
 
< 0.1%
36 4
 
< 0.1%
35 6
 
< 0.1%
34 1
 
< 0.1%

Postal Code
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct836
Distinct (%)0.5%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean98173.714
Minimum1730
Maximum99577
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-02-18T18:45:05.137316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1730
5-th percentile98006
Q198052
median98122
Q398371
95-th percentile98942
Maximum99577
Range97847
Interquartile range (IQR)319

Descriptive statistics

Standard deviation2442.5844
Coefficient of variation (CV)0.024880228
Kurtosis954.3603
Mean98173.714
Median Absolute Deviation (MAD)101
Skewness-30.096087
Sum1.6374885 × 1010
Variance5966218.6
MonotonicityNot monotonic
2024-02-18T18:45:05.677516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98052 4252
 
2.5%
98012 3115
 
1.9%
98033 2840
 
1.7%
98006 2663
 
1.6%
98004 2652
 
1.6%
98115 2553
 
1.5%
98074 2341
 
1.4%
98072 2312
 
1.4%
98188 2286
 
1.4%
98034 2237
 
1.3%
Other values (826) 139544
83.7%
ValueCountFrequency (%)
1730 1
< 0.1%
1731 1
< 0.1%
1824 1
< 0.1%
1908 1
< 0.1%
2842 1
< 0.1%
3804 1
< 0.1%
6355 1
< 0.1%
6371 1
< 0.1%
6379 2
< 0.1%
6385 1
< 0.1%
ValueCountFrequency (%)
99577 1
 
< 0.1%
99403 57
 
< 0.1%
99402 9
 
< 0.1%
99371 1
 
< 0.1%
99362 329
0.2%
99361 12
 
< 0.1%
99360 7
 
< 0.1%
99357 19
 
< 0.1%
99356 1
 
< 0.1%
99354 283
0.2%

Model Year
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.3418
Minimum1997
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-02-18T18:45:06.119047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1997
5-th percentile2014
Q12018
median2021
Q32023
95-th percentile2023
Maximum2024
Range27
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0014645
Coefficient of variation (CV)0.0014856222
Kurtosis0.45101207
Mean2020.3418
Median Absolute Deviation (MAD)2
Skewness-1.086228
Sum3.3699301 × 108
Variance9.0087894
MonotonicityNot monotonic
2024-02-18T18:45:06.667556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2023 51351
30.8%
2022 27592
16.5%
2021 18774
 
11.3%
2018 14151
 
8.5%
2020 11425
 
6.8%
2019 10860
 
6.5%
2017 8523
 
5.1%
2016 5518
 
3.3%
2015 4833
 
2.9%
2013 4455
 
2.7%
Other values (12) 9318
 
5.6%
ValueCountFrequency (%)
1997 1
 
< 0.1%
1998 1
 
< 0.1%
1999 3
 
< 0.1%
2000 7
 
< 0.1%
2002 2
 
< 0.1%
2003 1
 
< 0.1%
2008 20
 
< 0.1%
2010 23
 
< 0.1%
2011 782
0.5%
2012 1630
1.0%
ValueCountFrequency (%)
2024 3309
 
2.0%
2023 51351
30.8%
2022 27592
16.5%
2021 18774
 
11.3%
2020 11425
 
6.8%
2019 10860
 
6.5%
2018 14151
 
8.5%
2017 8523
 
5.1%
2016 5518
 
3.3%
2015 4833
 
2.9%

Make
Real number (ℝ)

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.009556
Minimum0
Maximum38
Zeros29
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-02-18T18:45:06.974294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q117
median33
Q333
95-th percentile36
Maximum38
Range38
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.153098
Coefficient of variation (CV)0.44595344
Kurtosis-0.82438343
Mean25.009556
Median Absolute Deviation (MAD)3
Skewness-0.87794483
Sum4171594
Variance124.39159
MonotonicityNot monotonic
2024-02-18T18:45:07.272478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
33 74834
44.9%
27 13848
 
8.3%
6 13072
 
7.8%
11 8577
 
5.1%
4 7196
 
4.3%
18 6995
 
4.2%
35 5812
 
3.5%
36 4717
 
2.8%
17 4100
 
2.5%
15 4057
 
2.4%
Other values (29) 23592
 
14.1%
ValueCountFrequency (%)
0 29
 
< 0.1%
1 3464
 
2.1%
2 8
 
< 0.1%
3 3
 
< 0.1%
4 7196
4.3%
5 245
 
0.1%
6 13072
7.8%
7 2878
 
1.7%
8 28
 
< 0.1%
9 801
 
0.5%
ValueCountFrequency (%)
38 3
 
< 0.1%
37 3962
 
2.4%
36 4717
 
2.8%
35 5812
 
3.5%
34 5
 
< 0.1%
33 74834
44.9%
32 788
 
0.5%
31 275
 
0.2%
30 3554
 
2.1%
29 1097
 
0.7%

Model
Real number (ℝ)

HIGH CORRELATION 

Distinct120
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14115.185
Minimum1
Maximum32822
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-02-18T18:45:07.607071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile360
Q11919
median6413
Q328926
95-th percentile32822
Maximum32822
Range32821
Interquartile range (IQR)27007

Descriptive statistics

Standard deviation13390.912
Coefficient of variation (CV)0.94868838
Kurtosis-1.6560601
Mean14115.185
Median Absolute Deviation (MAD)6053
Skewness0.40752751
Sum2.3544129 × 109
Variance1.7931654 × 108
MonotonicityNot monotonic
2024-02-18T18:45:07.900804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32822 32822
19.7%
28926 28926
17.3%
13274 13274
 
8.0%
7611 7611
 
4.6%
6413 6413
 
3.8%
5429 5429
 
3.3%
4825 4825
 
2.9%
3647 3647
 
2.2%
3161 3161
 
1.9%
3107 3107
 
1.9%
Other values (110) 57585
34.5%
ValueCountFrequency (%)
1 4
 
< 0.1%
2 10
 
< 0.1%
3 6
 
< 0.1%
5 10
 
< 0.1%
6 6
 
< 0.1%
8 8
 
< 0.1%
10 30
< 0.1%
11 22
< 0.1%
12 12
 
< 0.1%
13 26
< 0.1%
ValueCountFrequency (%)
32822 32822
19.7%
28926 28926
17.3%
13274 13274
8.0%
7611 7611
 
4.6%
6413 6413
 
3.8%
5429 5429
 
3.3%
4825 4825
 
2.9%
3647 3647
 
2.2%
3161 3161
 
1.9%
3107 3107
 
1.9%

Electric Vehicle Type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
130293 
1
36507 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166800
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 130293
78.1%
1 36507
 
21.9%

Length

2024-02-18T18:45:08.194372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-18T18:45:08.495064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 130293
78.1%
1 36507
 
21.9%

Most occurring characters

ValueCountFrequency (%)
0 130293
78.1%
1 36507
 
21.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166800
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 130293
78.1%
1 36507
 
21.9%

Most occurring scripts

ValueCountFrequency (%)
Common 166800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 130293
78.1%
1 36507
 
21.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 130293
78.1%
1 36507
 
21.9%

Clean Alternative Fuel Vehicle Eligibility
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1
83517 
0
64299 
2
18984 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166800
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row2
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 83517
50.1%
0 64299
38.5%
2 18984
 
11.4%

Length

2024-02-18T18:45:08.727462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-18T18:45:09.002245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 83517
50.1%
0 64299
38.5%
2 18984
 
11.4%

Most occurring characters

ValueCountFrequency (%)
1 83517
50.1%
0 64299
38.5%
2 18984
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166800
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 83517
50.1%
0 64299
38.5%
2 18984
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
Common 166800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 83517
50.1%
0 64299
38.5%
2 18984
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 83517
50.1%
0 64299
38.5%
2 18984
 
11.4%

Electric Range
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct102
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.508993
Minimum0
Maximum337
Zeros83517
Zeros (%)50.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-02-18T18:45:09.273628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q384
95-th percentile259
Maximum337
Range337
Interquartile range (IQR)84

Descriptive statistics

Standard deviation93.271747
Coefficient of variation (CV)1.516392
Kurtosis0.38709605
Mean61.508993
Median Absolute Deviation (MAD)0
Skewness1.3743532
Sum10259700
Variance8699.6188
MonotonicityNot monotonic
2024-02-18T18:45:09.565527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 83517
50.1%
215 6272
 
3.8%
220 4103
 
2.5%
25 3918
 
2.3%
84 3918
 
2.3%
238 3790
 
2.3%
21 3298
 
2.0%
32 3182
 
1.9%
208 2472
 
1.5%
53 2466
 
1.5%
Other values (92) 49864
29.9%
ValueCountFrequency (%)
0 83517
50.1%
6 935
 
0.6%
8 35
 
< 0.1%
9 21
 
< 0.1%
10 162
 
0.1%
11 3
 
< 0.1%
12 164
 
0.1%
13 358
 
0.2%
14 1109
 
0.7%
15 89
 
0.1%
ValueCountFrequency (%)
337 74
 
< 0.1%
330 318
 
0.2%
322 1671
1.0%
308 485
 
0.3%
293 443
 
0.3%
291 2335
1.4%
289 646
 
0.4%
270 275
 
0.2%
266 1400
0.8%
265 124
 
0.1%

Base MSRP
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1152.7232
Minimum0
Maximum845000
Zeros163437
Zeros (%)98.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-02-18T18:45:09.846800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum845000
Range845000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8661.0811
Coefficient of variation (CV)7.5135829
Kurtosis602.62985
Mean1152.7232
Median Absolute Deviation (MAD)0
Skewness12.846622
Sum1.9227422 × 108
Variance75014326
MonotonicityNot monotonic
2024-02-18T18:45:10.096559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 163437
98.0%
69900 1390
 
0.8%
31950 381
 
0.2%
52900 220
 
0.1%
32250 141
 
0.1%
54950 130
 
0.1%
59900 124
 
0.1%
39995 111
 
0.1%
36900 100
 
0.1%
44100 95
 
0.1%
Other values (21) 671
 
0.4%
ValueCountFrequency (%)
0 163437
98.0%
31950 381
 
0.2%
32250 141
 
0.1%
32995 3
 
< 0.1%
33950 72
 
< 0.1%
34995 67
 
< 0.1%
36800 54
 
< 0.1%
36900 100
 
0.1%
39995 111
 
0.1%
43700 11
 
< 0.1%
ValueCountFrequency (%)
845000 1
 
< 0.1%
184400 10
< 0.1%
110950 20
< 0.1%
109000 6
 
< 0.1%
102000 15
< 0.1%
98950 20
< 0.1%
91250 5
 
< 0.1%
90700 20
< 0.1%
89100 7
 
< 0.1%
81100 22
< 0.1%

Legislative District
Real number (ℝ)

Distinct49
Distinct (%)< 0.1%
Missing360
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean29.178941
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-02-18T18:45:10.373721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q118
median33
Q342
95-th percentile48
Maximum49
Range48
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.853534
Coefficient of variation (CV)0.50904978
Kurtosis-1.0877132
Mean29.178941
Median Absolute Deviation (MAD)11
Skewness-0.46529352
Sum4856543
Variance220.62746
MonotonicityNot monotonic
2024-02-18T18:45:10.694731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
41 10837
 
6.5%
45 10062
 
6.0%
48 9158
 
5.5%
1 7231
 
4.3%
5 7062
 
4.2%
36 6977
 
4.2%
11 6607
 
4.0%
46 6464
 
3.9%
43 6172
 
3.7%
37 4933
 
3.0%
Other values (39) 90937
54.5%
ValueCountFrequency (%)
1 7231
4.3%
2 1885
 
1.1%
3 824
 
0.5%
4 1385
 
0.8%
5 7062
4.2%
6 1593
 
1.0%
7 787
 
0.5%
8 1726
 
1.0%
9 930
 
0.6%
10 2879
 
1.7%
ValueCountFrequency (%)
49 2250
 
1.3%
48 9158
5.5%
47 3032
 
1.8%
46 6464
3.9%
45 10062
6.0%
44 4337
2.6%
43 6172
3.7%
42 2315
 
1.4%
41 10837
6.5%
40 3637
 
2.2%

DOL Vehicle ID
Real number (ℝ)

UNIQUE 

Distinct166800
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1724199 × 108
Minimum4385
Maximum4.7925477 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-02-18T18:45:10.992334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4385
5-th percentile1.0927191 × 108
Q11.7907406 × 108
median2.2440453 × 108
Q32.5134213 × 108
95-th percentile3.4421029 × 108
Maximum4.7925477 × 108
Range4.7925039 × 108
Interquartile range (IQR)72268068

Descriptive statistics

Standard deviation77274578
Coefficient of variation (CV)0.35570737
Kurtosis3.4968468
Mean2.1724199 × 108
Median Absolute Deviation (MAD)30844772
Skewness0.7260005
Sum3.6235965 × 1013
Variance5.9713604 × 1015
MonotonicityNot monotonic
2024-02-18T18:45:11.313119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1593721 1
 
< 0.1%
166073470 1
 
< 0.1%
148905543 1
 
< 0.1%
249787583 1
 
< 0.1%
220480571 1
 
< 0.1%
249691865 1
 
< 0.1%
131525467 1
 
< 0.1%
233740279 1
 
< 0.1%
144426796 1
 
< 0.1%
118183838 1
 
< 0.1%
Other values (166790) 166790
> 99.9%
ValueCountFrequency (%)
4385 1
< 0.1%
4777 1
< 0.1%
10286 1
< 0.1%
10734 1
< 0.1%
12050 1
< 0.1%
23145 1
< 0.1%
24629 1
< 0.1%
27702 1
< 0.1%
35325 1
< 0.1%
46112 1
< 0.1%
ValueCountFrequency (%)
479254772 1
< 0.1%
479114996 1
< 0.1%
478935460 1
< 0.1%
478934571 1
< 0.1%
478926346 1
< 0.1%
478925947 1
< 0.1%
478925163 1
< 0.1%
478924358 1
< 0.1%
478916028 1
< 0.1%
478910428 1
< 0.1%
Distinct835
Distinct (%)0.5%
Missing10
Missing (%)< 0.1%
Memory size1.3 MiB
2024-02-18T18:45:11.768904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length33
Median length32
Mean length28.439799
Min length24

Characters and Unicode

Total characters4743474
Distinct characters20
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique286 ?
Unique (%)0.2%

Sample

1st rowPOINT (-120.524012 46.5973939)
2nd rowPOINT (-122.817545 46.98876)
3rd rowPOINT (-122.1298876 47.4451257)
4th rowPOINT (-122.1873 47.820245)
5th rowPOINT (-122.2012521 47.3931814)
ValueCountFrequency (%)
point 166790
33.3%
47.67668 4252
 
0.8%
122.12302 4252
 
0.8%
122.1873 3115
 
0.6%
47.820245 3115
 
0.6%
122.20264 2840
 
0.6%
47.6785 2840
 
0.6%
122.16937 2663
 
0.5%
47.571015 2663
 
0.5%
122.201905 2652
 
0.5%
Other values (1660) 305188
61.0%
2024-02-18T18:45:12.518137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 492336
 
10.4%
1 377714
 
8.0%
4 335804
 
7.1%
. 333580
 
7.0%
333580
 
7.0%
7 314789
 
6.6%
5 301633
 
6.4%
3 203552
 
4.3%
6 202005
 
4.3%
8 197371
 
4.2%
Other values (10) 1651110
34.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2741994
57.8%
Uppercase Letter 833950
 
17.6%
Other Punctuation 333580
 
7.0%
Space Separator 333580
 
7.0%
Dash Punctuation 166790
 
3.5%
Open Punctuation 166790
 
3.5%
Close Punctuation 166790
 
3.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 492336
18.0%
1 377714
13.8%
4 335804
12.2%
7 314789
11.5%
5 301633
11.0%
3 203552
7.4%
6 202005
7.4%
8 197371
7.2%
9 172064
 
6.3%
0 144726
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
O 166790
20.0%
T 166790
20.0%
N 166790
20.0%
I 166790
20.0%
P 166790
20.0%
Other Punctuation
ValueCountFrequency (%)
. 333580
100.0%
Space Separator
ValueCountFrequency (%)
333580
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 166790
100.0%
Open Punctuation
ValueCountFrequency (%)
( 166790
100.0%
Close Punctuation
ValueCountFrequency (%)
) 166790
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3909524
82.4%
Latin 833950
 
17.6%

Most frequent character per script

Common
ValueCountFrequency (%)
2 492336
12.6%
1 377714
9.7%
4 335804
8.6%
. 333580
8.5%
333580
8.5%
7 314789
 
8.1%
5 301633
 
7.7%
3 203552
 
5.2%
6 202005
 
5.2%
8 197371
 
5.0%
Other values (5) 817160
20.9%
Latin
ValueCountFrequency (%)
O 166790
20.0%
T 166790
20.0%
N 166790
20.0%
I 166790
20.0%
P 166790
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4743474
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 492336
 
10.4%
1 377714
 
8.0%
4 335804
 
7.1%
. 333580
 
7.0%
333580
 
7.0%
7 314789
 
6.6%
5 301633
 
6.4%
3 203552
 
4.3%
6 202005
 
4.3%
8 197371
 
4.2%
Other values (10) 1651110
34.8%

Electric Utility
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35695.909
Minimum1
Maximum61337
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-02-18T18:45:12.861971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile674
Q19615
median33669
Q361337
95-th percentile61337
Maximum61337
Range61336
Interquartile range (IQR)51722

Descriptive statistics

Standard deviation22401.924
Coefficient of variation (CV)0.6275768
Kurtosis-1.312451
Mean35695.909
Median Absolute Deviation (MAD)27668
Skewness-0.17697044
Sum5.9540776 × 109
Variance5.018462 × 108
MonotonicityNot monotonic
2024-02-18T18:45:13.155448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61337 61337
36.8%
33669 33669
20.2%
29600 29600
17.7%
9615 9615
 
5.8%
7387 7387
 
4.4%
3805 3805
 
2.3%
2621 2621
 
1.6%
1449 1449
 
0.9%
1245 1245
 
0.7%
1177 1177
 
0.7%
Other values (55) 14895
 
8.9%
ValueCountFrequency (%)
1 4
 
< 0.1%
2 6
 
< 0.1%
3 3
 
< 0.1%
4 4
 
< 0.1%
5 15
 
< 0.1%
6 6
 
< 0.1%
15 45
< 0.1%
16 16
 
< 0.1%
18 18
 
< 0.1%
21 21
< 0.1%
ValueCountFrequency (%)
61337 61337
36.8%
33669 33669
20.2%
29600 29600
17.7%
9615 9615
 
5.8%
7387 7387
 
4.4%
3805 3805
 
2.3%
2621 2621
 
1.6%
1449 1449
 
0.9%
1245 1245
 
0.7%
1177 1177
 
0.7%

2020 Census Tract
Real number (ℝ)

SKEWED 

Distinct2088
Distinct (%)1.3%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.2977092 × 1010
Minimum1.0010201 × 109
Maximum5.6033 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-02-18T18:45:14.280517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.0010201 × 109
5-th percentile5.3011041 × 1010
Q15.303301 × 1010
median5.303303 × 1010
Q35.3053073 × 1010
95-th percentile5.3067012 × 1010
Maximum5.6033 × 1010
Range5.503198 × 1010
Interquartile range (IQR)20063300

Descriptive statistics

Standard deviation1.5697544 × 109
Coefficient of variation (CV)0.029630815
Kurtosis751.92796
Mean5.2977092 × 1010
Median Absolute Deviation (MAD)27702
Skewness-26.966776
Sum8.836314 × 1015
Variance2.4641288 × 1018
MonotonicityNot monotonic
2024-02-18T18:45:14.606555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.30330282 × 10101914
 
1.1%
5.30330285 × 10101001
 
0.6%
5.303303232 × 1010763
 
0.5%
5.30330262 × 1010713
 
0.4%
5.30330093 × 1010647
 
0.4%
5.30670112 × 1010624
 
0.4%
5.303303232 × 1010564
 
0.3%
5.303303222 × 1010545
 
0.3%
5.303302501 × 1010532
 
0.3%
5.306105211 × 1010529
 
0.3%
Other values (2078) 158963
95.3%
ValueCountFrequency (%)
1001020100 2
< 0.1%
1081041901 1
< 0.1%
1097006803 1
< 0.1%
1117030352 1
< 0.1%
2020000206 1
< 0.1%
4013115900 1
< 0.1%
4013216901 1
< 0.1%
4013610301 1
< 0.1%
4013610302 1
< 0.1%
4013610500 1
< 0.1%
ValueCountFrequency (%)
5.60330001 × 10101
 
< 0.1%
5.307794001 × 10105
 
< 0.1%
5.307794001 × 10103
 
< 0.1%
5.307794 × 10102
 
< 0.1%
5.307794 × 10107
 
< 0.1%
5.307794 × 10107
 
< 0.1%
5.307794 × 10103
 
< 0.1%
5.30770034 × 101033
< 0.1%
5.30770032 × 101040
< 0.1%
5.30770031 × 101020
< 0.1%

Interactions

2024-02-18T18:44:55.739307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:43:56.066525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:01.148597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:04.970381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:09.716028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:14.671358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:18.600339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:22.949876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:28.009439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:31.911584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:36.210705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:41.023795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:45.848266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:49.796827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:56.036588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:43:56.353983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:01.398268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:05.230143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:10.035430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:14.949430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:18.876728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:23.221240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:28.295758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:32.179609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:36.493478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:41.297645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:46.126512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:50.177328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:56.306303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:43:56.665098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:01.653135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:05.495137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:10.377185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:15.213038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:19.144548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:23.605275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:28.562443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:32.446995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:36.821914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:41.555525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:46.405300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:50.645919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:56.585249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:43:57.059014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:01.920869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:05.772904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:10.806033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:15.471407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:19.406287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:24.040792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:28.855471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:32.738902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:37.158359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:41.833865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:46.683414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:51.038229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:56.859768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:43:57.424902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:02.178072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:06.059798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:11.209793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:15.730327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:19.669807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:24.451100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:29.125032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:33.015526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:37.559735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:42.103672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:46.957343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:51.457615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:57.145262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:43:57.869089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:02.452501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:06.333615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:11.601807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:16.002199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:20.364677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:24.853493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:29.402650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:33.284003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:37.932638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:42.411597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:47.238209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:51.913536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:57.431065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:43:58.326783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:02.731132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:06.622393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:12.031942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:16.288013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:20.649820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:25.241797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:29.695754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:33.556388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:38.373191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:42.697517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:47.546570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:52.382902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:57.721423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:43:58.767950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:03.013848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:06.916586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:12.426850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:16.579907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:20.940415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:25.670588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:29.990082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:33.839220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:38.793050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:42.976140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:47.829226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:52.818438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:58.013393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:43:59.248617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:03.284435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:08.057302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:12.845011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:16.857570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:21.224551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:26.115319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:30.279153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:34.113683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:39.215764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:43.268842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:48.121273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:53.270478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:58.307401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:43:59.739641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:03.579273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:08.350198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:13.295159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:17.185760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:21.538454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:26.571102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:30.576529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:34.787300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:39.670186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:43.552240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:48.423553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:53.611271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:58.575669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:00.042137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:03.853110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:08.632527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:13.567501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:17.456357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:21.806966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:26.854418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:30.843560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:35.055017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:39.975313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:44.067451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:48.717791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:53.905535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:58.878428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:00.315139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:04.127290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:08.924397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:13.835078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:17.741521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:22.079198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:27.131974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:31.104095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:35.324558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:40.243563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:44.541531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:48.978874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:54.192358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:59.159458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:00.598198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:04.415610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:09.187672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:14.127579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:18.018655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:22.367678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:27.420292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:31.363653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:35.615273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:40.507334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:44.806487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:49.246108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:55.159575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:59.454045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:00.889035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:04.709684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:09.459842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:14.404644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:18.327976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:22.661470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:27.722157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:31.643268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:35.918802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:40.768074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:45.478422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:49.523642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-18T18:44:55.447439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-02-18T18:45:14.889035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2020 Census TractBase MSRPCityClean Alternative Fuel Vehicle EligibilityCountyDOL Vehicle IDElectric RangeElectric UtilityElectric Vehicle TypeLegislative DistrictMakeModelModel YearPostal CodeStateVIN (1-10)
2020 Census Tract1.0000.000-0.3650.009-0.3120.011-0.0150.0890.013-0.187-0.0160.0160.0110.0580.0800.009
Base MSRP0.0001.000-0.0050.024-0.002-0.0350.116-0.0030.0220.0110.003-0.109-0.190-0.003-0.005-0.158
City-0.365-0.0051.0000.0440.608-0.000-0.0200.1590.0650.3940.0510.0410.028-0.3860.0790.045
Clean Alternative Fuel Vehicle Eligibility0.0090.0240.0441.0000.0130.092-0.7170.0260.743-0.0180.067-0.0820.455-0.0050.000-0.001
County-0.312-0.0020.6080.0131.0000.013-0.0520.6360.0990.4350.0660.0750.074-0.7460.0850.084
DOL Vehicle ID0.011-0.035-0.0000.0920.0131.000-0.1600.0280.078-0.017-0.008-0.0600.348-0.0060.0120.069
Electric Range-0.0150.116-0.020-0.717-0.052-0.1601.000-0.0660.525-0.004-0.113-0.127-0.6970.055-0.007-0.195
Electric Utility0.089-0.0030.1590.0260.6360.028-0.0661.0000.0890.2580.0580.0900.093-0.7030.0780.095
Electric Vehicle Type0.0130.0220.0650.7430.0990.0780.5250.0891.000-0.068-0.296-0.489-0.1590.109-0.014-0.445
Legislative District-0.1870.0110.394-0.0180.435-0.017-0.0040.258-0.0681.0000.0480.036-0.014-0.338NaN0.031
Make-0.0160.0030.0510.0670.066-0.008-0.1130.058-0.2960.0481.0000.4940.092-0.091-0.0070.424
Model0.016-0.1090.041-0.0820.075-0.060-0.1270.090-0.4890.0360.4941.0000.064-0.1090.0020.822
Model Year0.011-0.1900.0280.4550.0740.348-0.6970.093-0.159-0.0140.0920.0641.000-0.0610.0160.180
Postal Code0.058-0.003-0.386-0.005-0.746-0.0060.055-0.7030.109-0.338-0.091-0.109-0.0611.0000.079-0.105
State0.080-0.0050.0790.0000.0850.012-0.0070.078-0.014NaN-0.0070.0020.0160.0791.0000.007
VIN (1-10)0.009-0.1580.045-0.0010.0840.069-0.1950.095-0.4450.0310.4240.8220.180-0.1050.0071.000

Missing values

2024-02-18T18:44:59.893030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-18T18:45:00.782102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-02-18T18:45:01.737827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

VIN (1-10)CountyCityStatePostal CodeModel YearMakeModelElectric Vehicle TypeClean Alternative Fuel Vehicle EligibilityElectric RangeBase MSRPLegislative DistrictDOL Vehicle IDVehicle LocationElectric Utility2020 Census Tract
089436394298902.0201498010087014.01593721POINT (-120.524012 46.5973939)12455.307700e+10
14604240584298513.020173354290020002.0257167501POINT (-122.817545 46.98876)336695.306701e+10
258659446174298058.0202344311220011.0224071816POINT (-122.1298876 47.4451257)613375.303303e+10
3981957054404298012.02023301622010021.0260084653POINT (-122.1873 47.820245)336695.306105e+10
41708659426024298031.02020332892600322033.0253771913POINT (-122.2012521 47.3931814)613375.303303e+10
53555226554298370.02024423011039023.0259427829POINT (-122.64177 47.737525)336695.303594e+10
61155229884298367.02018728781033026.0477087012POINT (-122.6847073 47.50524)336695.303509e+10
77555226554298370.020176641300238023.0214494213POINT (-122.64177 47.737525)336695.303509e+10
823155229884298366.02018332892600215026.0280785123POINT (-122.639265 47.5373)336695.303509e+10
93865945814298019.020184191900114045.0129133343POINT (-121.9810747 47.7377962)613375.303303e+10
VIN (1-10)CountyCityStatePostal CodeModel YearMakeModelElectric Vehicle TypeClean Alternative Fuel Vehicle EligibilityElectric RangeBase MSRPLegislative DistrictDOL Vehicle IDVehicle LocationElectric Utility2020 Census Tract
166790386594278314298102.02024151751033043.0258782814POINT (-122.32226 47.64058)296005.303301e+10
16679118403927824298225.020241731611221042.0261031214POINT (-122.486115 48.761615)38055.307300e+10
16679214818426644298221.0201327132740075010.0259757998POINT (-122.615305 48.501275)336695.305794e+10
16679378659450284298033.0202229608010048.0199014224POINT (-122.20264 47.6785)613375.303302e+10
16679475597214298925.02022182043010013.0212130950POINT (-121.1761632 47.24106)336695.303798e+10
16679513431225734299223.02013111778121906.0239527123POINT (-117.369705 47.62637)26215.306300e+10
1667964108659448764298074.020213332822010045.0148715479POINT (-122.0313266 47.6285782)613375.303303e+10
166797375195707374298275.020223332822010021.0220504406POINT (-122.299965 47.94171)336695.306104e+10
16679810709284298564.02013648251038020.0156418475POINT (-122.487535 46.5290135)3245.304197e+10
166799591297218524298332.0201733761100210026.0169045789POINT (-122.589645 47.342345)73875.305307e+10